QS World University Rankings Analysis Project Milestone 1

Weike ZHANG, Ruoqin JI

March 2024

For more details, datasets, and analysis scripts, visit our GitHub webpage.

Collaboration Plan¶

Teamwork Approach:

  • Responsibilities: Ruoqin JI focused on data curation and exploratory analysis using Python, while I handled model development and hypothesis testing.
  • Technologies: We used a private GitHub repo for code sharing/version control and Visual Studio Code with Live Share for real-time collaborative coding.
  • Communication: Scheduled bi-weekly Zoom meetings for progress discussions and utilized Slack for daily communication.

Execution:

  • Our work distribution leveraged individual strengths, enhancing project efficiency.
  • GitHub and Live Share facilitated seamless integration of our contributions.
  • Regular Zoom and Slack interactions ensured alignment and prompt problem-solving.

This strategy ensured our project's success, reflecting our coordinated understanding and effort.

Project Outline¶

Introduction¶

The QS World University Rankings are a globally recognized framework for evaluating higher education institutions. This project will analyze ranking trends from 2022 to 2024 to uncover patterns and determinants of university performance. The findings will serve as an empirical guide for stakeholders in the education sector.

Objectives:¶

  • To identify trends and shifts in university rankings over the specified years.
  • To understand the impact of various performance metrics on the rankings.
  • To provide insights for educational institutions aiming to improve their standings.

Data and Summary Statistics¶

I. Data Sources (Extraction, Transform, and Load)¶

  • Description of the datasets for 2022, 2023, and 2024, including data structure and collection methodology.
  • Data size and completeness, with an emphasis on any data preprocessing conducted.

II. Summary Statistics¶

  • Computation of summary statistics for critical variables to establish a baseline understanding of the dataset's characteristics.

Measure and Variable Definition¶

  • In-depth explanation of QS ranking metrics.
  • Discussion on how each metric is quantified and its presumed influence on the overall rankings.

Exploratory Data Analysis (EDA)¶

I. Ranking Trends¶

  • Tracking shifts in rankings across the years and pinpointing outliers.
  • Identifying institutions with notable improvements or declines.

II. Metric Correlations¶

  • Investigating the interrelationship between ranking metrics using correlation analysis.
  • Visualizations to showcase the strength and direction of these relationships.

III. Geographic Trends¶

  • Geographic analysis of the distribution of top-ranked institutions.
  • Examination of regional performance and disparities.

IV. Internationalization¶

  • Evaluating the influence of international faculty and student presence on ranking outcomes.

Empirical Results¶

I. Regression Analysis¶

  • Linear regression models to estimate the effect of ranking metrics on the overall score.
  • Discussion of the model's assumptions, validations, and any transformations applied to the data.

II. Predictive Modelling¶

  • Developing predictive models to forecast future rankings based on identified trends.
  • Validation of predictive accuracy through back-testing with historical data.

Conclusion and Implications¶

  • Synthesis of key findings and their implications for universities and policymakers.
  • Discussion of the study's limitations and suggestions for further research.

Additional Sections:¶

  • Methodology: Detailed justification of statistical methods used.
  • Ethical Considerations: Reflection on the ethical aspects of ranking interpretations.
  • Peer Review: Strategy for peer review to validate findings.

Appendices:¶

  • Detailed tables, additional analyses, and a glossary of terms used throughout the project.

References:¶

  • Detailed bibliography citing data sources, literature, and methodologies referenced.

Data and Summary Statistics¶

I. Data Sources (Extraction, Transform, and Load)¶

QS World University Rankings The QS World University Rankings provide a comprehensive evaluation of over 1,000 higher education institutions globally. Sourced from Quacquarelli Symonds (QS), these rankings are recognized worldwide for their depth of research and breadth of data regarding university performance. The datasets for 2022, 2023, and 2024, accessible through the QS website, form the primary basis of our analysis. These tables offer detailed insights into various performance metrics such as academic reputation, employer reputation, faculty-student ratio, citations per faculty, international faculty, and international students scores. By analyzing these datasets, we aim to uncover trends, evaluate shifts in rankings, and identify the determinants of university performance across the specified years.

  • View the QS World University Rankings 2022 Report
  • QS World University Rankings 2023 Result Tables - Excel
  • QS World University Rankings 2024 Results Table - Excel

QS World University Rankings Metrics Explained¶

The QS ranking methodology utilizes several metrics to gauge university performance, each capturing a distinct aspect of university excellence:

  • Academic Reputation Score (40% weight): Derived from a global academic survey, this score reflects the perceived research quality and academic standing of an institution.

  • Employer Reputation Score (10% weight): Based on a survey of employers, this score indicates the employability and preparedness of graduates in the workforce.

  • Faculty Student Score (20% weight): This metric measures the faculty-to-student ratio, providing insight into the teaching and learning environment of the university.

  • Citations per Faculty Score (20% weight): A measure of research impact, this score is calculated based on the average citations per faculty member, indicating research influence and quality.

  • International Faculty Score (5% weight): This score assesses the diversity of the faculty by measuring the proportion of international faculty members at the institution.

  • International Students Score (5% weight): Similarly, this score evaluates the diversity of the student body by looking at the percentage of international students.

  • Overall Score: A composite score that combines all individual metrics, representing a summarized assessment of a university's overall ranking performance.

In [1]:
import pandas as pd
import os
import sys
import matplotlib.pyplot as plt
import seaborn as sns
In [2]:
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
In [3]:
!git clone https://github.com/weike2001/ds
Cloning into 'ds'...
remote: Enumerating objects: 35, done.
remote: Counting objects: 100% (35/35), done.
remote: Compressing objects: 100% (31/31), done.
remote: Total 35 (delta 5), reused 0 (delta 0), pack-reused 0
Receiving objects: 100% (35/35), 4.55 MiB | 6.64 MiB/s, done.
Resolving deltas: 100% (5/5), done.
In [4]:
import pandas as pd

# Set the paths to the Excel files in the cloned repository
file_path_2022 = '/content/ds/data/2022_QS_World_University_Rankings_Results_public_version.xlsx'
file_path_2023 = '/content/ds/data/2023 QS World University Rankings V2.1 (For qs.com).xlsx'
file_path_2024 = '/content/ds/data/2024 QS World University Rankings 1.2 (For qs.com).xlsx'

# Read the data into pandas DataFrames
df_2022 = pd.read_excel(file_path_2022)
df_2023 = pd.read_excel(file_path_2023)
df_2024 = pd.read_excel(file_path_2024)

# Assuming you want to save these DataFrames as CSV files in the same directory
csv_file_path_2022 = file_path_2022.replace('.xlsx', '.csv')
csv_file_path_2023 = file_path_2023.replace('.xlsx', '.csv')
csv_file_path_2024 = file_path_2024.replace('.xlsx', '.csv')

# Save the DataFrames as CSV files
df_2022.to_csv(csv_file_path_2022, index=False)
df_2023.to_csv(csv_file_path_2023, index=False)
df_2024.to_csv(csv_file_path_2024, index=False)

Adjust columns in each csv form

In [14]:
import pandas as pd

# Define the new specific column names
specific_column_names_2022 = [
    'National Rank', 'Regional Rank', '2022 Rank', '2021 Rank', 'Institution Name',
    'Location Code', 'Country/Territory', 'Size', 'Focus', 'Research Intensity',
    'Age Band', 'Status', 'Academic Reputation Score', 'Academic Reputation Rank',
    'Employer Reputation Score', 'Employer Reputation Rank', 'Faculty Student Score',
    'Faculty Student Rank', 'Citations per Faculty Score', 'Citations per Faculty Rank',
    'International Faculty Score', 'International Faculty Rank', 'International Students Score',
    'International Students Rank', 'Overall Score'
]

specific_column_names_2023 = [
    '2023 Rank', '2022 Rank', 'Institution Name', 'Location Code', 'Country/Territory',
    'Size', 'Focus', 'Research Intensity', 'Age Band', 'Status',
    'Academic Reputation Score', 'Academic Reputation Rank',
    'Employer Reputation Score', 'Employer Reputation Rank',
    'Faculty Student Score', 'Faculty Student Rank',
    'Citations per Faculty Score', 'Citations per Faculty Rank',
    'International Faculty Score', 'International Faculty Rank',
    'International Students Score', 'International Students Rank',
    'International Research Network Score', 'International Research Network Rank',
    'Employment Outcomes Score', 'Employment Outcomes Rank',
    'Overall Score'
]

specific_column_names_2024 = [
    '2024 Rank', '2023 Rank', 'Institution Name', 'Location Code', 'Country/Territory',
    'Size', 'Focus', 'Research Intensity', 'Status',
    'Academic Reputation Score', 'Academic Reputation Rank',
    'Employer Reputation Score', 'Employer Reputation Rank',
    'Faculty Student Score', 'Faculty Student Rank',
    'Citations per Faculty Score', 'Citations per Faculty Rank',
    'International Faculty Score', 'International Faculty Rank',
    'International Students Score', 'International Students Rank',
    'International Research Network Score', 'International Research Network Rank',
    'Employment Outcomes Score', 'Employment Outcomes Rank',
    'Sustainability Score', 'Sustainability Rank',
    'Overall Score'
]

print(len(specific_column_names_2024))
# Reading the CSV files into Pandas DataFrames
df_2022 = pd.read_csv(csv_file_path_2022, skiprows = 4, names=specific_column_names_2022)
df_2023 = pd.read_csv(csv_file_path_2023, skiprows = 4, names=specific_column_names_2023)
df_2024 = pd.read_csv(csv_file_path_2024, skiprows = 4, names=specific_column_names_2024)

df_2022.head()
28
Out[14]:
National Rank Regional Rank 2022 Rank 2021 Rank Institution Name Location Code Country/Territory Size Focus Research Intensity ... Employer Reputation Rank Faculty Student Score Faculty Student Rank Citations per Faculty Score Citations per Faculty Rank International Faculty Score International Faculty Rank International Students Score International Students Rank Overall Score
0 1 1 1 1 Massachusetts Institute of Technology (MIT) US United States M CO VH ... 4 100.0 12 100.0 6 100.0 45 91.4 105 100
1 1 1 2 5 University of Oxford UK United Kingdom L FC VH ... 3 100.0 5 96.0 34 99.5 83 98.5 52 99.5
2 2 2 3= 2 Stanford University US United States L FC VH ... 5 100.0 9 99.9 10 99.8 73 67.0 208 98.7
3 2 2 3= 7 University of Cambridge UK United Kingdom L FC VH ... 2 100.0 10 92.1 48 100.0 57 97.7 64 98.7
4 3 3 5 3 Harvard University US United States L FC VH ... 1 99.1 37 100.0 3 84.2 188 70.1 196 98

5 rows × 25 columns

In [15]:
df_2023.head()
Out[15]:
2023 Rank 2022 Rank Institution Name Location Code Country/Territory Size Focus Research Intensity Age Band Status ... Citations per Faculty Rank International Faculty Score International Faculty Rank International Students Score International Students Rank International Research Network Score International Research Network Rank Employment Outcomes Score Employment Outcomes Rank Overall Score
0 1 1 Massachusetts Institute of Technology (MIT) US United States M CO VH 5.0 B ... 5 100.0 54 90.0 109 96.1 58 100.0 3 100
1 2 3= University of Cambridge UK United Kingdom L FC VH 5.0 A ... 55 100.0 60 96.3 70 99.5 6 100.0 9 98.8
2 3 3= Stanford University US United States L FC VH 5.0 B ... 9 99.8 74 60.3 235 96.3 55 100.0 2 98.5
3 4 2 University of Oxford UK United Kingdom L FC VH 5.0 A ... 64 98.8 101 98.4 54 99.9 3 100.0 7 98.4
4 5 5 Harvard University US United States L FC VH 5.0 B ... 2 76.9 228 66.9 212 100.0 1 100.0 1 97.6

5 rows × 27 columns

In [16]:
df_2024.head()
Out[16]:
2024 Rank 2023 Rank Institution Name Location Code Country/Territory Size Focus Research Intensity Status Academic Reputation Score ... International Faculty Rank International Students Score International Students Rank International Research Network Score International Research Network Rank Employment Outcomes Score Employment Outcomes Rank Sustainability Score Sustainability Rank Overall Score
0 1 1 Massachusetts Institute of Technology (MIT) US United States M CO VH B 100.0 ... 56 88.2 128 94.3 58 100.0 4 95.2 51 100
1 2 2 University of Cambridge UK United Kingdom L FC VH A 100.0 ... 64 95.8 85 99.9 7 100.0 6 97.3 33= 99.2
2 3 4 University of Oxford UK United Kingdom L FC VH A 100.0 ... 110 98.2 60 100.0 1 100.0 3 97.8 26= 98.9
3 4 5 Harvard University US United States L FC VH B 100.0 ... 210 66.8 223 100.0 5 100.0 1 96.7 39 98.3
4 5 3 Stanford University US United States L FC VH B 100.0 ... 78 51.2 284 95.8 44 100.0 2 94.4 63 98.1

5 rows × 28 columns

In this section, we focus on preparing the 'Overall Score' data from the QS World University Rankings for 2022, 2023, and 2024. The preparation involves two key steps:

  1. Replacing Missing Values: We convert missing values, originally represented as hyphens ('-'), to NaN (Not a Number) to standardize the dataset for numerical analysis.
  2. Converting to Numeric: The 'Overall Score' column is converted from string type to floating-point numbers, facilitating statistical operations and analysis.

Objectives:

  • Clean and standardize the data for accurate analysis.
  • Enable computation of descriptive statistics and facilitate trend analysis across years.
  • Assess the completeness of the data to ensure robust analytical outcomes.

This data preparation is essential for analyzing global university ranking trends and setting the stage for further in-depth examination of university performances.

In [6]:
import pandas as pd
import numpy as np

# Replace hyphens with NaN and convert the column to numeric
df_2022['Overall Score'] = pd.to_numeric(df_2022['Overall Score'].replace('-', np.nan), errors='coerce')
df_2023['Overall Score'] = pd.to_numeric(df_2023['Overall Score'].replace('-', np.nan), errors='coerce')
df_2024['Overall Score'] = pd.to_numeric(df_2024['Overall Score'].replace('-', np.nan), errors='coerce')

# Now, 'Overall Score' will be a float column with NaNs where there were hyphens - .

II. Summary Statistics¶

In our analysis of the QS World University Rankings datasets spanning 2022 to 2024, we direct our attention to a curated selection of metrics that significantly influence a university's prestige and global ranking. The evaluation encompasses:

  • Academic Reputation Score: A gauge of a university's academic eminence as recognized by peers.
  • Employer Reputation Score: A reflection of the institution's graduate employability and readiness for the professional world.
  • Citations per Faculty Score: An index of research influence and scholarly impact.
  • International Faculty Score: A measure of the institution's success in fostering a diverse and global faculty.
  • International Students Score: An indicator of the university's ability to attract a worldwide student body.
  • Overall Score: A comprehensive score that embodies all individual metrics, offering a summarized assessment of a university's worldwide standing and performance.

For these pivotal metrics, we compute the mean, standard deviation, median, minimum, and maximum values to provide a distilled overview of university performance. This analysis will shed light on the average achievements, consistency, and range within these critical areas, offering stakeholders a succinct and strategic insight into the dynamics shaping university rankings.

In [11]:
import pandas as pd

df_2022.describe()
Out[11]:
Age Band Academic Reputation Score Employer Reputation Score Faculty Student Score Citations per Faculty Score International Faculty Score International Students Score Overall Score
count 1300.000000 1300.000000 1300.000000 1299.000000 1300.000000 1228.000000 1275.000000 501.000000
mean 4.011538 21.552462 22.193000 31.907313 26.293308 26.503746 28.119059 44.767066
std 0.988318 23.315627 24.535947 28.564402 28.299027 35.429502 31.211629 18.961269
min 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 24.100000
25% 3.000000 6.200000 5.100000 9.400000 3.400000 1.700000 3.750000 29.600000
50% 4.000000 11.900000 11.950000 20.600000 13.400000 5.400000 13.200000 38.600000
75% 5.000000 25.925000 29.625000 47.950000 43.400000 44.425000 44.450000 55.400000
max 5.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000
In [9]:
df_2023.describe()
Out[9]:
Age Band Academic Reputation Score Employer Reputation Score Faculty Student Score Citations per Faculty Score International Faculty Score International Students Score International Research Network Score Employment Outcomes Score Overall Score
count 1411.000000 1422.000000 1421.000000 1420.000000 1417.000000 1324.000000 1365.000000 1409.000000 1410.000000 500.000000
mean 4.008505 20.124684 20.657143 29.997113 24.529358 31.659517 26.545348 49.570121 26.186809 44.619400
std 0.965320 22.802706 24.027928 28.172207 27.910952 34.170817 30.896854 30.205439 26.201036 18.655057
min 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 24.200000
25% 3.000000 5.400000 4.400000 8.200000 3.100000 4.800000 3.300000 21.600000 6.700000 29.800000
50% 4.000000 10.800000 10.300000 18.250000 11.100000 13.750000 10.800000 47.700000 15.500000 38.550000
75% 5.000000 23.775000 27.000000 43.500000 39.400000 55.075000 40.500000 77.600000 36.900000 54.500000
max 5.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000
In [10]:
df_2024.describe()
Out[10]:
Academic Reputation Score Employer Reputation Score Faculty Student Score Citations per Faculty Score International Faculty Score International Students Score International Research Network Score Employment Outcomes Score Sustainability Score Overall Score
count 1498.000000 1497.000000 1474.000000 1474.000000 1372.000000 1418.000000 1494.000000 1474.000000 1398.000000 602.000000
mean 20.132043 19.806880 28.643894 23.940163 30.948834 25.575035 23.967938 20.016961 25.412017 40.879900
std 22.365895 23.764625 27.843868 28.075573 34.247562 30.867149 30.371277 20.241410 31.010557 19.181335
min 1.600000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 19.800000
25% 6.000000 4.100000 7.500000 2.800000 4.300000 3.000000 1.200000 8.225000 1.400000 25.700000
50% 10.900000 9.500000 16.750000 10.400000 13.050000 9.850000 6.850000 11.700000 8.400000 34.550000
75% 23.100000 25.500000 41.900000 37.900000 52.725000 38.075000 40.375000 22.475000 42.525000 51.300000
max 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000

Measure and Variable Definition¶

This section is dedicated to a comprehensive examination of the QS World University Rankings' metrics. We aim to dissect each component of the ranking system to provide an intricate understanding of how universities are evaluated and ranked on the global stage.

The QS ranking framework employs a set of multifaceted metrics, each designed to quantify distinct aspects of university performance. These metrics are:

  • Academic Reputation Score (40%): Derived from a global survey, reflecting the university's standing in the academic community.
  • Employer Reputation Score (10%): Based on employer surveys, indicating the quality and employability of the institution's graduates.
  • Faculty/Student Ratio (20%): A metric that assesses the faculty-to-student ratio, providing insights into the educational environment.
  • Citations per Faculty (20%): This measures the average number of citations per faculty member, serving as an indicator of research impact.
  • International Faculty Score (5%) and International Students Score (5%): Both these scores evaluate the university's internationalization by measuring the diversity of faculty and student bodies.

The Overall Score represents a consolidated assessment derived from these individual metrics, dictating the university's ranking.

In This Section:¶

  • We will analyze each metric in detail, understanding the data sources, methodology, and computation.
  • Discuss the weight each metric carries and its hypothesized impact on the overall ranking.
  • Conduct a comparative evaluation across various universities to identify strengths and weaknesses relative to these metrics.
  • Reflect on the historical evolution of these metrics and their definitions to appreciate changes in higher education quality assessment.

Through this deep dive into the QS ranking metrics, we seek to elucidate the nuances that underpin university rankings, providing a clear guide for institutions aiming to enhance their global standing.

In [12]:
import matplotlib.pyplot as plt
import seaborn as sns

qs_metrics_weights = {
    'Academic Reputation Score': {"weight": 0.40},
    'Employer Reputation Score': {"weight": 0.10},
    'Faculty Student Score': {"weight": 0.20},
    'Citations per Faculty Score': {"weight": 0.20},
    'International Faculty Score': {"weight": 0.05},
    'International Students Score': {"weight": 0.05},
}

def create_grid_layout_without_definitions(df, metrics_info, year):
    # Set up the figure with subplots
    fig, axes = plt.subplots(2, 3, figsize=(20, 10))  # Adjust figure size as needed
    axes = axes.ravel()
    palette = sns.color_palette("coolwarm", len(metrics_info))

    # Plot each metric in the grid
    for ax, (metric, info), color in zip(axes, metrics_info.items(), palette):
        weight = info['weight']
        sns.histplot(df[metric], kde=True, ax=ax, color=color, alpha=0.7, linewidth=0.5)
        ax.set_title(f"{metric} ({weight*100}%)", fontsize=10)
        ax.set_xlabel('Score', fontsize=9)

    # Add a main title and adjust layout
    plt.suptitle(f'Distribution of QS Ranking Metrics for {year}', fontsize=16)
    plt.tight_layout(rect=[0, 0.03, 1, 0.95])  # Adjust the layout
    plt.show()

# Example usage with the 2022 dataset
create_grid_layout_without_definitions(df_2022, qs_metrics_weights, '2022')
create_grid_layout_without_definitions(df_2023, qs_metrics_weights, '2023')
create_grid_layout_without_definitions(df_2024, qs_metrics_weights, '2024')

The QS World University Rankings across 2022, 2023, and 2024 highlight a consistent pattern among key metrics that determine institutional prestige. The Academic Reputation Score, as the most weighted metric, displays a persistent skew towards a select echelon of universities, emphasizing the enduring recognition of established institutions. Variability in Employer Reputation and Faculty Student ratios across these years reflects evolving perceptions of graduate quality and educational resource allocation. The metrics for Research Impact and Internationalization, though varied, indicate a continuous commitment to global engagement and scholarly output. Collectively, these trends reaffirm the comprehensive criteria of the QS rankings and the sustained excellence among leading universities on a global scale.

Exploratory Data Analysis (EDA)¶

I. Ranking Trends¶

  • Tracking shifts in rankings across the years and pinpointing outliers.
  • Identifying institutions with notable improvements or declines.

Geographic Distribution of QS Ranked Universities¶

To gain a deeper understanding of the global landscape of higher education as reflected in the QS World University Rankings, we employ choropleth maps to visualize the distribution of ranked universities by country for the years 2022, 2023, and 2024. This geographic analysis allows us to observe trends, patterns, and potentially the regional dynamics influencing higher education excellence on a global scale.

The function create_choropleth_map is crafted to:

  1. Count Universities by Country: For each year, it calculates the number of universities within each country that appear in the QS rankings.
  2. Generate a Choropleth Map: Utilizing Plotly Express, it creates an interactive map highlighting countries based on the count of their ranked universities. The intensity of the color corresponds to the number of universities, providing a clear visual representation of higher education hubs worldwide.

Here's a brief overview of the function and its application:

In [13]:
import pandas as pd
import plotly.express as px
import plotly

#enable_plotly_in_cell()
def create_choropleth_map(dataframe, column_name, title):

    # Generate a dictionary of value counts for the specified column
    sample_data = dataframe[column_name].value_counts().to_dict()

    # Convert the dictionary into a DataFrame
    df_counts = pd.DataFrame(list(sample_data.items()), columns=['Country', 'University_Count'])
    #print(df_counts)
    # Create the choropleth map
    fig = px.choropleth(df_counts,
                        locations="Country",
                        locationmode='country names',
                        color="University_Count",
                        color_continuous_scale=px.colors.sequential.Reds,  # Reds color scale
                        title=title)

    # Update the layout
    fig.update_layout(
        geo=dict(
            showframe=False,
            showcoastlines=False,
            projection_type='equirectangular'
        )
    )
    # Show the figure
    fig.show(renderer="notebook")

# Use the function with your DataFrame and column
create_choropleth_map(df_2022, 'Country/Territory', 'Number of Universities per Country in 2022')
create_choropleth_map(df_2023, 'Country/Territory', 'Number of Universities per Country in 2023')
create_choropleth_map(df_2024, 'Country/Territory', 'Number of Universities per Country in 2024')

The choropleth maps for the QS World University Rankings from 2022 through 2024 consistently show that North America and Europe maintain a dominant presence with the highest number of globally recognized universities. This steadfast pattern underscores the concentration of academic prestige and resources in these regions. Despite the passage of time, the geographic distribution of leading institutions remains relatively unchanged, highlighting a persistent imbalance in global educational prominence. The continuity of this trend into 2024 further suggests that while there is global progress in higher education, efforts to diversify and enhance representation in the rankings could be strengthened to reflect a more inclusive global academic landscape.

Reference¶

Our excel files come from links below:

  • View the QS World University Rankings 2022 Report
  • QS World University Rankings 2023 Result Tables - Excel
  • QS World University Rankings 2024 Results Table - Excel